Neural network learning is an optimisation problem. Multilayered, feedforward static neural networks are well trained by using error-backpropagation algorithm. Genetic algorithms are also used for the optimisation problem solving. In this master thesis a genetic algorithm for neural network training is developed. Its benefit in this field is mesaured by comparing it to error back-propagation algorithm. The hybrid method – combination of both the error back-propagation and genethic algorithm is also shown, and its quality is evaluated. And finally, modified genetic algorithm for imbalanced learning data set is presented and tested. Genetic, error back-propagation and hybrid algorithms were implemented. Genetic algorithm is tested for different chromosome representations, population size, mutation probability and the way mutation is applied. All implemented learning algorithms are tested on neural network for pattern classification in 2 classes. Testing results indicate that the genetic algorithm can be used successfully for the neural network training. Training results with genetic algorithm with floating-point chromosome representation were much better than results achieved with error back propagation. At the same time, training results achieved with genetic algorithm with floating point chromosome representation were slightly worse. Hybrid method did not show any progress when compared to training with the genetic algorithm only. Genetic learnnig for imbalanced learning data sets is easier to implement than an adequate error back-propagation. In these cases training with the genethic algorithm was also successful.